Hybrid Quantum-Classical Neural Architecture Search

2026-05-18

AI Summary

This work addresses the challenge that the performance and efficiency of hybrid quantum-classical neural networks are highly sensitive to architectural design, which is difficult to optimize manually under the constraints of Noisy Intermediate-Scale Quantum (NISQ) hardware and limited resources. To this end, the study introduces, for the first time, FLOPs-aware neural architecture search (NAS) into this domain, proposing an end-to-end trainable framework that jointly optimizes data encoding, quantum circuit structure, measurement strategy, and the coupling between classical and quantum modules. The approach significantly enhances computational efficiency while preserving model accuracy, yielding architectures better suited for practical deployment on real-world NISQ devices.

Abstract

Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with parameterized quantum circuits in an end-to-end trainable framework. However, their performance and efficiency depend strongly on architectural choices such as data encoding, circuit structure, measurement design, and the coupling between classical and quantum modules. This makes manual design increasingly difficult, especially when hardware limitations and resource constraints must also be taken into account. In this paper, we study the foundations of HQNNs and neural architecture search (NAS), discuss how NAS extends to quantum and hybrid settings, and demonstrate FLOPs-aware search (where FLOPs serve as a proxy for computational complexity), as an important hardware-aware direction for building HQNNs that are not only accurate but also computationally efficient and practically deployable.

Problem

• Hybrid Quantum-Classical Neural Networks
• Neural Architecture Search
• NISQ
• Hardware-aware Design
• Computational Efficiency

Innovation

• Hybrid Quantum-Classical Neural Networks
• Neural Architecture Search
• Hardware-aware Search
• FLOPs-aware Optimization
• NISQ